We find that the failure of current state-of-the-art models is partly due to the training pipeline, , regarding all regions that overlap little with the annotated foreground objects as background.
Even if the background includes hidden objects—background objects that are visible but unlabeled—as in Fig. REF , the models are trained not to detect them, which prevents the models from learning general objectness. To address this, Kim  [1]} proposed to learn the localization quality of region proposals instead of classifying them as foreground vs. background. Their approach samples object proposals close to the ground truth and learns to estimate the corresponding localization quality. While partially mitigating the issue, this approach still needs to carefully set the overlap threshold for positive/negative sampling and risks suppressing hidden objects as non-objects.
